devtools::install_github("timelyportfolio/parcoords")

# import necessary libraries
library(dplyr)
library(ggplot2)
library(ggmap)
library(readr)
library(zoo)
library(wordcloud)
library(skimr)
library(viridis)
library(GGally)
library(ggthemes)
library(parcoords)

Introduction

(Carlo)

Description of Data

Analysis of Data Quality

# read in file with number of restaurants in each city by cuisine
# note: the SQL query for this file included only a particular set of cuisines
restaurants_by_cuisine <- read_csv("~/EDAV_project/Data/number of restaurants by cuisine.csv")

# concatenate city and state columns into one column called location
restaurants_by_cuisine <- mutate(restaurants_by_cuisine, location=paste0(city,", ",state)) %>% subset(select=c(5,1:4))

# get number of restaurants for each city
r_by_city <- restaurants_by_cuisine %>% group_by(location) %>%
                    summarize(number= sum(NumberOfRestaurants))

r_by_city_filter <- r_by_city %>% filter(number > 1000)

ggplot(r_by_city_filter, aes(x = reorder(location, number), y = number)) + geom_col() + coord_flip() + ggtitle("Number of Restaurants in Cities with >1000 Restaurants") + xlab("City") + ylab("Number of Restaurants")

# Calculated weighted gini index for each city (measure of both diversity and quantity of restaurants in a city)

# compute percent of restaurants in each location and category
num_cuisines <- restaurants_by_cuisine %>% group_by(location,category) %>% summarise(count = sum(NumberOfRestaurants, na.rm = TRUE)) %>% mutate(perc = round(count/sum(count),4))

gini <- function(x, na.rm = TRUE) {
  index <- 1 - sum(x**2) 
  return(index)
}

# calculate gini index
num_cuisines <- num_cuisines %>% group_by(location) %>% summarise(gini = gini(perc))

num_cuisines <- num_cuisines[order(-num_cuisines$gini),]
head(num_cuisines, 10)
## # A tibble: 10 x 2
##    location         gini
##    <chr>           <dbl>
##  1 Toronto, ON     0.923
##  2 Mississauga, ON 0.919
##  3 Edinburgh, MLN  0.913
##  4 Ajax, ON        0.911
##  5 Pickering, ON   0.909
##  6 Vaughan, ON     0.906
##  7 Montréal, QC    0.904
##  8 North York, ON  0.903
##  9 Oakville, ON    0.899
## 10 Brossard, QC    0.899
# calculate weighted gini index
weighted_gini_city <- plyr::join(r_by_city, num_cuisines, by="location")
weighted_gini_city <- mutate(weighted_gini_city, weighted_gini = gini*number/sum(number))

weighted_gini_city <- weighted_gini_city[order(-weighted_gini_city$weighted_gini),]

head(weighted_gini_city, 10)
##            location number      gini weighted_gini
## 568     Toronto, ON   4903 0.9225066    0.12645167
## 250   Las Vegas, NV   4020 0.8753205    0.09837537
## 414     Phoenix, AZ   2454 0.8401270    0.05763850
## 334    Montréal, QC   1681 0.9039136    0.04248032
## 77    Charlotte, NC   1615 0.8607311    0.03886272
## 419  Pittsburgh, PA   1449 0.8556579    0.03466265
## 320 Mississauga, ON   1001 0.9186229    0.02570778
## 507  Scottsdale, AZ   1056 0.8302322    0.02451075
## 89    Cleveland, OH    827 0.8412700    0.01945065
## 277     Madison, WI    693 0.8692630    0.01684138
# get cities with top 10 weighted gini index
weighted_gini_city_filter <- weighted_gini_city %>% top_n(10, weighted_gini)

ggplot(weighted_gini_city_filter, aes(x = reorder(location, weighted_gini), y = weighted_gini)) + geom_col() + coord_flip() + ggtitle("Cities with Top 10 Weighted Gini Index") + xlab("City") + ylab("Weighted Gini Index")

# visualizing diversity of restaurants in cities with top 10 weighted gini index

top_10_cities <- unique(weighted_gini_city_filter$location)

r_by_city_cuisine <- filter(restaurants_by_cuisine, location %in% top_10_cities)

#refactoring location columns
r_by_city_cuisine$location <- factor(r_by_city_cuisine$location)

ggplot(r_by_city_cuisine, aes(x=category, y=NumberOfRestaurants)) + geom_col() + facet_wrap(~location, scales="free_y", ncol=3) + theme(axis.text.x = element_text(angle=90, hjust=1)) + ggtitle("Cuisines by City") + xlab("City") + ylab("Number of Restaurants")

Main Analysis (Exploratory Data Analysis)

Geographical Distribution of Restaurants

# reading in restaurants in Toronto file
restaurants_in_Toronto <- read_csv("~/EDAV_project/restaurants in Toronto.csv")

# reading in cuisine categories from text file
cuisine_cat <- read.delim("Cat_List_cuisines_only.txt", header = TRUE)

# subset restaurants dataset by categories in text file
restaurants <- restaurants_in_Toronto %>% filter(category %in% cuisine_cat$Category)

# finding 20 most common cuisines
cuisine_counts <- restaurants %>% group_by(category) %>% summarise(count=n()) %>% arrange(-count) %>% top_n(20,count)

mostcommon20_cuisines <- unique(cuisine_counts$category)

# filtering out all cuisines except for 20 most common cuisines in Toronto for mapping and graphing
cuisines_toronto <- restaurants %>% filter(category %in% mostcommon20_cuisines)

# confirm that there are no missing values in id, category, latitude, and longitude columns
skim(cuisines_toronto)
## Skim summary statistics
##  n obs: 4290 
##  n variables: 13 
## 
## Variable type: character 
##      variable missing complete    n min max empty n_unique
##       address      12     4278 4290   8  64     0     3031
##      category       0     4290 4290   4  22     0       20
##          city       0     4290 4290   7   7     0        2
##            id       0     4290 4290  22  22     0     3213
##          name       0     4290 4290   2  46     0     2721
##  neighborhood     835     3455 4290   7  29     0       71
##   postal_code       9     4281 4290   3   7     0     1822
##         state       0     4290 4290   2   2     0        1
## 
## Variable type: integer 
##      variable missing complete    n  mean    sd p0 p25 p50 p75 p100
##       is_open       0     4290 4290  1     0     1   1   1   1    1
##  review_count       0     4290 4290 59.81 94.51  3  10  29  69 1494
##      hist
##  ▁▁▁▇▁▁▁▁
##  ▇▁▁▁▁▁▁▁
## 
## Variable type: numeric 
##   variable missing complete    n   mean    sd     p0    p25    p50    p75
##   latitude       0     4290 4290  43.69 0.049  43.59  43.65  43.67  43.71
##  longitude       0     4290 4290 -79.39 0.066 -79.7  -79.42 -79.39 -79.37
##      stars       0     4290 4290   3.45 0.71    1      3      3.5    4   
##    p100     hist
##   43.84 ▁▅▇▂▁▁▁▁
##  -79.14 ▁▁▁▂▇▂▁▁
##    5    ▁▁▂▆▇▇▂▁
# analysis of NA values in neighborhood column by cuisine

skim(restaurants)
## Skim summary statistics
##  n obs: 5389 
##  n variables: 13 
## 
## Variable type: character 
##      variable missing complete    n min max empty n_unique
##       address      13     5376 5389   8  64     0     3425
##      category       0     5389 5389   4  22     0       94
##          city       0     5389 5389   7   7     0        2
##            id       0     5389 5389  22  22     0     3637
##          name       0     5389 5389   2  46     0     3105
##  neighborhood    1074     4315 5389   7  29     0       71
##   postal_code      12     5377 5389   3   7     0     1998
##         state       0     5389 5389   2   2     0        1
## 
## Variable type: integer 
##      variable missing complete    n  mean    sd p0 p25 p50 p75 p100
##       is_open       0     5389 5389  1     0     1   1   1   1    1
##  review_count       0     5389 5389 61.33 96.47  3  10  30  71 1494
##      hist
##  ▁▁▁▇▁▁▁▁
##  ▇▁▁▁▁▁▁▁
## 
## Variable type: numeric 
##   variable missing complete    n   mean    sd     p0    p25    p50    p75
##   latitude       0     5389 5389  43.69 0.05   43.59  43.65  43.67  43.71
##  longitude       0     5389 5389 -79.39 0.067 -79.7  -79.42 -79.39 -79.37
##      stars       0     5389 5389   3.48 0.7     1      3      3.5    4   
##    p100     hist
##   43.88 ▁▇▅▂▂▂▁▁
##  -79.14 ▁▁▁▂▇▂▁▁
##    5    ▁▁▂▅▇▇▂▁
cuisines_toronto_na <- cuisines_toronto %>% group_by(category) %>% summarise(num_nas = sum(is.na(neighborhood)))

cuisines_toronto_na <- plyr::join(cuisines_toronto_na, cuisine_counts, by="category")

cuisines_toronto_perc <- cuisines_toronto_na %>% mutate(perc_nas = num_nas/count)

ggplot(cuisines_toronto_perc, aes(x = category, y = perc_nas)) + geom_col() + theme(axis.text.x = element_text(angle=90, hjust=1))

ggplot(cuisine_counts, aes(x = category, y = count)) + geom_col() + theme(axis.text.x = element_text(angle=90, hjust=1))

Distribution of all restaurants in Toronto (removing duplicates)

cuisines_toronto_dedup <- cuisines_toronto[-13] %>% distinct()

qmplot(longitude, latitude, data = cuisines_toronto_dedup, maptype = "toner-lite", color = I("red"), size=I(0.5), alpha=I(.5)) + ggtitle("Restaurants in Toronto")

# mapping neighborhood NA values

cuisines_toronto_dedup <- cuisines_toronto[-13] %>% distinct()

qmplot(longitude, latitude, data = cuisines_toronto_dedup, maptype = "toner-lite", color = ifelse(is.na(neighborhood), I("NA"), I("Not NA")), size=I(0.5), alpha=I(.5)) + ggtitle("Restaurants in Toronto")  + scale_color_colorblind() + theme(legend.title=element_blank())

# + scale_color_viridis(discrete=TRUE)

Distribution of restaurants in Toronto by cuisine

qmplot(longitude, latitude, data = cuisines_toronto, maptype = "toner-lite", color=I("red"), size=I(0.05), alpha=I(0.5)) + ggtitle("Restaurants in Toronto by Cuisine") + facet_wrap(~category)

Distribution of restaurants in Toronto by rating

cuisines_toronto_rating_dedup <- cuisines_toronto_dedup %>% mutate(rating = cut(stars, breaks=c(0,2,3,4,5), labels=c("Very Bad","Bad","Good","Very Good")))

qmplot(longitude, latitude, data = cuisines_toronto_rating_dedup, maptype = "toner-lite", color = I("red"), size=I(0.5), alpha=I(.5)) + ggtitle("Restaurants in Toronto by Rating") + facet_wrap(~rating)

Distribution of restaurants with “Good” and “Very Good” rating by cuisine

cuisines_toronto_rating <- cuisines_toronto %>% mutate(rating = cut(stars, breaks=c(0,2,3,4,5), labels=c("Very Bad","Bad","Good","Very Good"))) 

cuisines_toronto_good <- cuisines_toronto_rating %>% filter(rating %in% c("Good","Very Good"))

qmplot(longitude, latitude, data = cuisines_toronto_good, maptype = "toner-lite", color=I("red"), size=I(0.05), alpha=I(0.5)) + ggtitle('"Good" and "Very Good" Restaurants in Toronto by Cuisine') + facet_wrap(~category)

Distribution of restaurants in Toronto by Number of Check-ins

# reading in check-in data
check_in <- read_csv("~/EDAV_project/Data/restaurants checkins Toronto.csv")

check_in <- check_in %>% group_by(check_in$business_id) %>% summarise(n_checkins = sum(count, na.rm=TRUE))
colnames(check_in)[1] <- "id"

cuisines_toronto_check_ins_dedup <- plyr::join(cuisines_toronto_rating_dedup, check_in, by = "id")

# checking for missing values
skim(cuisines_toronto_check_ins_dedup$n_checkins)
## Skim summary statistics
## 
## Variable type: integer 
##                                     variable missing complete    n   mean
##  cuisines_toronto_check_ins_dedup$n_checkins     471     2742 3213 128.41
##      sd p0   p25 p50 p75 p100     hist
##  258.14  1 13.25  44 137 5755 ▇▁▁▁▁▁▁▁
# remove restaurants where n_checkins is na
cuisines_toronto_check_ins_dedup <- cuisines_toronto_check_ins_dedup[!is.na(cuisines_toronto_check_ins_dedup$n_checkins),]

skim(cuisines_toronto_check_ins_dedup$n_checkins)
## Skim summary statistics
## 
## Variable type: integer 
##                                     variable missing complete    n   mean
##  cuisines_toronto_check_ins_dedup$n_checkins       0     2742 2742 128.41
##      sd p0   p25 p50 p75 p100     hist
##  258.14  1 13.25  44 137 5755 ▇▁▁▁▁▁▁▁
# using summary provided by skim to choose cutoff points for checkin categories
skim(cuisines_toronto_check_ins_dedup$n_checkins)
## Skim summary statistics
## 
## Variable type: integer 
##                                     variable missing complete    n   mean
##  cuisines_toronto_check_ins_dedup$n_checkins       0     2742 2742 128.41
##      sd p0   p25 p50 p75 p100     hist
##  258.14  1 13.25  44 137 5755 ▇▁▁▁▁▁▁▁
cuisines_toronto_check_ins_dedup <- cuisines_toronto_check_ins_dedup %>% mutate(checkin_cat = cut(n_checkins, breaks=c(-Inf,5,30,110,Inf), labels=c("Very Low","Low","High","Very High")))

qmplot(longitude, latitude, data = cuisines_toronto_check_ins_dedup, maptype = "toner-lite", color = I("red"), size=I(0.5), alpha=I(.5)) + ggtitle("Restaurants in Toronto by Number of Check-ins") + facet_wrap(~checkin_cat)

Distribution of restaurants with “High” and “Very High” number of check-ins by cuisine

# joining checkin data with cuisines_toronto_rating
cuisines_toronto_check_ins <- plyr::join(cuisines_toronto_rating, check_in, by = "id")

# checking for missing values
skim(cuisines_toronto_check_ins$n_checkins)
## Skim summary statistics
## 
## Variable type: integer 
##                               variable missing complete    n   mean     sd
##  cuisines_toronto_check_ins$n_checkins     498     3792 4290 155.11 339.98
##  p0 p25 p50 p75 p100     hist
##   1  14  50 159 5755 ▇▁▁▁▁▁▁▁
# remove restaurants where n_checkins is na
cuisines_toronto_check_ins <- cuisines_toronto_check_ins[!is.na(cuisines_toronto_check_ins$n_checkins),]

cuisines_toronto_check_ins <- cuisines_toronto_check_ins %>% mutate(checkin_cat = cut(n_checkins, breaks=c(-Inf,5,30,110,Inf), labels=c("Very Low","Low","High","Very High"))) 

cuisines_toronto_high <- cuisines_toronto_check_ins %>% filter(checkin_cat %in% c("High","Very High"))

qmplot(longitude, latitude, data = cuisines_toronto_high, maptype = "toner-lite", color=I("red"), size=I(0.05), alpha=I(0.5)) + ggtitle('Restaurants in Toronto with "High" and "Very High" Number of Check-ins by Cuisine') + facet_wrap(~category)

Distribution of cuisines in Toronto by neighborhood

# remove na values in neighborhood column and graph number of restaurants by cuisine in each neighborhood with >= 70 restaurants

pop_neighborhood <- cuisines_toronto[!is.na(cuisines_toronto$neighborhood), ] %>%
                    group_by(neighborhood) %>%
                    mutate(neigh_pop =n()) %>%
                    filter(neigh_pop >=10) %>%
                    ungroup() %>%
                    filter(neigh_pop >= 70) %>%
                    group_by(neighborhood, category) %>%
                    summarize(count = n())

ggplot(pop_neighborhood, aes(x = category, y = count))+
  geom_col()+
  coord_flip()+
  facet_wrap(~neighborhood)+
  ggtitle("Distribution of Cuisines by Neighborhood")

Sentiment, rating, and check-ins

Relationship between sentiment, rating, and check-ins

# add sentiment and check-in data to restaurants dataset

# adding in check-in data
restaurants_full <- plyr::join(restaurants, check_in, by = "id")

# reading in sentiment data
sentiment_toronto <- read_csv("~/EDAV_project/Data/sentiment_reviews_Toronto.csv")

# rename column name with sentiment value
colnames(sentiment_toronto)[11] <- "sentiment"

# get average sentiment for each restaurant
mean_sentiment <- sentiment_toronto %>% group_by(business_id) %>% summarise(avg_sentiment = mean(sentiment))

# add sentiment information to restaurants dataset
colnames(mean_sentiment)[1] <- "id"
restaurants_full <- plyr::join(restaurants_full, mean_sentiment, by = "id")

restaurants_full_dedup <- restaurants_full[-13] %>% distinct()

# remove outlier in n_checkins
restaurants_full_dedup <- restaurants_full_dedup %>% subset(n_checkins<2500) 

# remove na values in n_checkins and avg_sentiment
restaurants_full_dedup <- restaurants_full_dedup[!is.na(restaurants_full_dedup$n_checkins),]
restaurants_full_dedup <- restaurants_full_dedup[!is.na(restaurants_full_dedup$avg_sentiment),]

parallel_data <- restaurants_full_dedup[,c(10,13,14)]

colnames(parallel_data)[1] <- "Average Stars"
colnames(parallel_data)[2] <- "Number of Check-ins"
colnames(parallel_data)[3] <- "Average Sentiment"

# parallel coordinate plot of rating, sentiment, and check-ins
parcoords(parallel_data
  , rownames = F
  , brushMode = "1d-axes"
  , reorderable = T
  , color = list(
    colorBy = "Average Stars"
    , colorScale = htmlwidgets::JS("d3.scale.category10()")))
# ggparcoord(restaurants_full_dedup, columns = c(10,14,13), alphaLines = .1, scale = "uniminmax")

Star distribution for top 20 cuisine types

# read in restaurants in Toronto file as dataframe
restaurants <- read_csv("~/EDAV_project/restaurants in Toronto.csv")

# read in sentiment information
toronto = read_csv("~/EDAV_project/Data/sentiment_reviews_Toronto.csv")

# read in categories from text file
relevant_cats <- read.delim("Cat_List_cuisines_only.txt", header = TRUE)

# subset restaurants dataset by categories in text file
restaurants <- restaurants[restaurants$category %in% relevant_cats$Category,]

# obtain mapping of business ids and categories
categories <- restaurants[c(1,13)]

# remove user id from toronto sentiment dataset and rename "business id" column as "id"
data <- toronto[-1]
colnames(data)[1] <- "id"

# join toronto sentiment dataset (without user id) with restaurants dataset which has rating and other info
revs_cat <- plyr::join(data, restaurants, by = "id")

# remove rows with missing values in category column
revs_cat <- subset(revs_cat, !is.na(revs_cat$category))

# refactor category and stars column
revs_cat$category <- as.factor(revs_cat$category)
revs_cat$stars <- as.factor(revs_cat$stars)

# obtained counts by category and number of stars and percent for each star level within a category
temp <- revs_cat %>% group_by(revs_cat$category, revs_cat$stars) %>% summarise(cnt = n()) %>%
  mutate(perc = round(cnt/sum(cnt),4))

most_freq <- revs_cat %>% group_by(revs_cat$category) %>% summarise(cnt = n())
top_20 <- head(most_freq[order(-most_freq$cnt),], 20)[1]
subset <- revs_cat[revs_cat$category %in% top_20$`revs_cat$category`,]
temp <- subset %>% group_by(subset$category, subset$stars) %>% summarise(cnt = n()) %>%mutate(perc = round(cnt/sum(cnt),4))


# plotting top 20 cuisine types
ggplot(data = temp, aes(temp$`subset$stars`, perc)) + geom_col(fill = "darkblue", colour = "black") + labs(y = "Cuisine Type", x = "Stars") + coord_flip() + facet_wrap(~`subset$category`, scales = "free") + ggtitle("Stars by Cuisine Type")

Review Sentiment of top 20 cusines

ggplot(subset, aes(category, text_1)) + geom_boxplot() + coord_flip() + labs(y = "Review Sentiment", x = "Cuisine Type")

Review sentiment of Top 5, Bottom 5, Middle 5

top_5 <- head(most_freq[order(-most_freq$cnt),], 5)[1]
top_5$indicator <- "Top_5"
bottom_5 <- tail(most_freq[order(-most_freq$cnt),], 5)[1]
bottom_5$indicator <- "Bottom_5"
middle_5 <- most_freq[round(nrow(most_freq)/2,0):(round(nrow(most_freq)/2,0) + 4),][1]
middle_5$indicator <- "Middle_5"
sample <- rbind(bottom_5, top_5, middle_5)
colnames(sample)[1] <- "category"
subset <- subset(revs_cat, revs_cat$category %in% sample$category)
subset <- plyr::join(subset, sample, by = "category")
ggplot(subset, aes(category, text_1)) + geom_boxplot() + coord_flip() + labs(y = "Review Sentiment", x = "Cuisine Type") + facet_wrap(~indicator, scales = "free_y")

Find cuisines with highest and lowest average ratings

revs_cat$stars <- as.numeric(revs_cat$stars)
revs_cat <- revs_cat[order(as.Date(revs_cat$date, format="%Y-%m-%d")),]
revs_cat$ma_rating <- rollmeanr(revs_cat[,3],7,fill = NA)
revs_cat$ma_rating[is.na(revs_cat$ma_rating)] <- mean(revs_cat$ma_rating, na.rm = TRUE)
rating_by_cuisine <- revs_cat %>% group_by(revs_cat$category) %>% summarise(avg_rating = mean(ma_rating,
  na.rm = TRUE)) %>% arrange(desc(avg_rating))
top_10 <- head(rating_by_cuisine, 10)[1]
top_10$indicator <- "Top_10"
bottom_10 <- tail(rating_by_cuisine, 10)[1]
bottom_10$indicator <- "Bottom_10"
sample <- rbind(bottom_10, top_10)
subset <- subset(revs_cat, revs_cat$category %in% sample$`revs_cat$category`)
colnames(sample)[1] <- "category"
subset <- plyr::join(subset, sample, by = "category")

ggplot(subset, aes(category, ma_rating)) + geom_boxplot() + coord_flip() + labs(y = "Average Review", x =
  "Cuisine Type") + facet_wrap(~indicator, scales="free_y")

Case study:

# read in dataset of all reviews for Toronto businesses, text_1 column is the sentiment value 
# sentiment value calculated using python library
toronto = read_csv("~/EDAV_project/Data/sentiment_reviews_Toronto.csv")

# obtained number of reviews for each business id
most_rev <- data.frame(table(toronto$business_id)) 

# arranged business ids in decreasing order of number of reviews and converted business id column to character
most_rev <- most_rev[order(-most_rev$Freq),]
most_rev$Var1 <- as.character(most_rev$Var1)

# obtained business id with greatest number of reviews
var <- most_rev$Var1[1]

# obtain all reviews for business id with greatest number of reviews and order by date
reviews <- subset(toronto, business_id == var)
reviews <- reviews[order(as.Date(reviews$date, format="%Y-%m-%d")),]

# add year and month columns
reviews$year <- as.numeric(format(as.Date(reviews$date, format="%Y-%m-%d"),"%Y"))
reviews$month <- as.numeric(format(as.Date(reviews$date, format="%Y-%m-%d"),"%m"))

# convert elite column to dummy variable
reviews$elite <- as.factor(reviews$elite)
levels(reviews$elite) <- c(0,1)

# trim columns of review data frame
reviews <- reviews[c("business_id", "date", "stars", "elite", "text_1", "year", "month")]
reviews$date <- as.Date(reviews$date)
a) correlation between rating and sentiment
avg_sent <- reviews %>% group_by(stars) %>% summarize(mean = mean(text_1, na.rm = TRUE))
ggplot(avg_sent, aes(stars, mean)) + geom_col(color = "darkblue", fill = "darkblue") + xlab("Stars") +
  ylab("Average Review Sentiment")

b) review sentiment of elite users
elite_sent <- toronto %>% group_by(elite) %>% summarize(mean = mean(text_1, na.rm = TRUE))
ggplot(elite_sent, aes(elite, mean)) + geom_col(color = "darkblue", fill = "darkblue", width = 0.5) + xlab("Elite") + ylab("Average Review Sentiment")

c) word clouds for good reviews (sentiment >= 0.5 stars) and bad reviews (sentiment <= -0.5)

setwd("~/EDAV_project/Data")
good_revs <- read_csv("good_revs.csv")
bad_revs <-read_csv("bad_revs.csv")
set.seed(1234)
wordcloud(words = good_revs$word, freq = good_revs$freq, min.freq = 300,
          max.words=100, random.order=FALSE, rot.per=0.35, 
          colors=brewer.pal(8, "Dark2"))

wordcloud(words = bad_revs$word, freq = bad_revs$freq, min.freq = 5000,
          max.words=100, random.order=FALSE, rot.per=0.35, 
          colors=brewer.pal(8, "Dark2"))

d) sentiment progression over time
# Stationary Sentiment Progression over Time

elite <- subset(reviews, reviews$elite == 1)
ggplot(reviews, aes(date, text_1)) + geom_line(color='grey') + scale_x_date(date_labels = ("%b-%Y"), limits =
  as.Date(c(reviews$date[1], reviews$date[nrow(reviews)]))) + xlab("Date") + ylab("Sentiment") + ylim(-1,1) +
  geom_point(data = elite, aes(date, text_1), color='darkblue', shape = 5, size = 0.5) + ggtitle("Sentiment over
  Time")

reviews_2017 <- subset(reviews, reviews$date > as.Date("2017-06-01"))

# Stationary Graph for Sentiment Progression June - December 2017
elite <- subset(reviews_2017, reviews_2017$elite == 1)
ggplot(reviews_2017, aes(date, text_1)) + geom_line(color = 'grey') + scale_x_date(date_labels = ("%b-%Y"),
  limits = as.Date(c(reviews_2017$date[1], reviews_2017$date[nrow(reviews_2017)]))) + xlab("Date") +
  ylab("Sentiment") + ylim(-1,1) + geom_point(data = elite, aes(date, text_1), color='darkblue', shape = 5, size
  = 0.5) + ggtitle("Sentiment Progression from June to December 2017")

Executive Summary (Presentation-style)

Interactive Component

Conclusion